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Procode: A Machine-Learning Tool to Support (Re-)coding of Free-Texts of Occupations and Industries.

Nenad Savic1, Nicolas Bovio1, Fabien Gilbert2

  • 1Department for Health, Work and Environment, Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Route de la Corniche 2, CH-1066 Epalinges-Lausanne, Switzerland.

Annals of Work Exposures and Health
|June 19, 2021
PubMed
Summary

Procode is a free web tool that uses machine learning for automatic occupational coding. It accurately classifies French job titles (PCS) and activities (NAF) using Complement Naïve Bayes, aiding data analysis.

Keywords:
Naïve Bayescross-validationepidemiologymachine learningoccupational classifications

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Area of Science:

  • Occupational Health
  • Data Science
  • Sociology

Background:

  • Accurate occupational coding is crucial for labor market analysis and public health research.
  • Manual coding of free-text occupational data is time-consuming and prone to errors.
  • Existing tools for automatic coding may lack accuracy or comprehensive classification support.

Purpose of the Study:

  • To introduce Procode, a novel, free web-tool for automated occupational data coding.
  • To evaluate the performance of the Complement Naïve Bayes (CNB) machine-learning algorithm for classifying French occupational data.
  • To describe the tool's current functionalities, including recoding between classifications, and outline future development goals.

Main Methods:

  • Development of a web-tool, Procode, implementing the Complement Naïve Bayes (CNB) machine-learning algorithm.
  • Training the CNB model using approximately 30,000 free-text occupational entries with pre-assigned French Classification of Occupations (PCS) and Nomenclature of Activities (NAF) codes.
  • Performance evaluation using 5-fold cross-validation to assess prediction accuracy for PCS and NAF codes.

Main Results:

  • Procode achieved accurate classification code prediction rates of 57-81% for PCS and 63-83% for NAF.
  • The tool successfully integrates a recoding function between PCS and NAF classifications, initially via a search of existing crosswalks.
  • The study demonstrates the feasibility and effectiveness of using CNB for automated occupational coding.

Conclusions:

  • Procode offers a valuable, free resource for automating the coding of occupational free-text data.
  • The tool demonstrates promising accuracy in classifying French occupational data, supporting research and administrative tasks.
  • Future development will focus on expanding classification support and enhancing recoding functionalities.